RRepoGEO

REPOGEO REPORT · LITE

jax-ml/scaling-book

Default branch main · commit c0c4e1d1 · scanned 5/23/2026, 10:57:47 PM

GitHub: 1,006 stars · 145 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface jax-ml/scaling-book, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 to explicitly state it's a textbook

    Why:

    CURRENT
    # How To Scale Your Model
    COPY-PASTE FIX
    # How To Scale Your Model: An Online Textbook on Scaling LLMs on TPUs
  • mediumtopics#2
    Add topics that clarify the repo's format as a textbook

    Why:

    CURRENT
    jax, llm-inference, llms, roofline, tpus
    COPY-PASTE FIX
    jax, llm-inference, llms, roofline, tpus, textbook, deep-learning-guide, llm-guide
  • lowabout#3
    Enhance the 'About' description to emphasize its textbook nature

    Why:

    CURRENT
    Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
    COPY-PASTE FIX
    The official repository for "How To Scale Your Model", a comprehensive online textbook demystifying the art of scaling LLMs on TPUs. Read the full book at our homepage.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface jax-ml/scaling-book
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch FSDP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch FSDP · recommended 2×
  2. DeepSpeed · recommended 1×
  3. Megatron-LM · recommended 1×
  4. TensorFlow Distributed Strategy API · recommended 1×
  5. Accelerate · recommended 1×
  • CATEGORY QUERY
    How to optimize performance and scale very large deep learning models effectively?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP
    2. DeepSpeed
    3. Megatron-LM
    4. TensorFlow Distributed Strategy API
    5. Accelerate
    6. Ray Train
    7. Colossal-AI

    AI recommended 7 alternatives but never named jax-ml/scaling-book. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Explain hardware architecture and parallelism schemes for efficient large model deployment.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA A100/H100
    2. AMD Instinct MI250X/MI300X
    3. Google Cloud TPUs
    4. Cerebras Wafer-Scale Engine (WSE)
    5. Graphcore IPU
    6. PyTorch DDP
    7. NVIDIA Megatron-LM (NVIDIA/Megatron-LM)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. PyTorch FSDP
    10. JAX (google/jax)
    11. TensorFlow (tensorflow/tensorflow)
    12. ROCm (ROCm/ROCm)

    AI recommended 12 alternatives but never named jax-ml/scaling-book. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of jax-ml/scaling-book?
    pass
    AI did not name jax-ml/scaling-book — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts jax-ml/scaling-book in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jax-ml/scaling-book explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo jax-ml/scaling-book solve, and who is the primary audience?
    pass
    AI did not name jax-ml/scaling-book — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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jax-ml/scaling-book — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite